Efficient and robust high-speed neural networks for cell image classification
a high-speed, neural network technology, applied in the field of cell sorting, can solve the problems that all existing methods do not meet the speed requirements of typical applications including image activated cell sorting, and achieve the effects of high recall implementation, high accuracy, and high speed
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[0015]A shallow two-convolutional-layer neural network architecture has been designed and established suitable for very high-speed real-time cell image classification. In addition, the algorithm is able to minimize the common trade-off pitfall between precision and recall to achieve high precision with high recall.
[0016]The neural networks with cell image classification address unmet needs of many cell image classification applications—mainly two key requirements: high accuracy and high speed. The existing classical machine learning-based method (e.g., boosting, svm with manually engineered features) and deep learning-based method cannot met the speed requirements. The method described herein not only runs very fast but also achieves high accuracy because of the shallow neural network architecture designed by architecture search techniques and the novel precision-recall trade-off module.
[0017]The neural networks with cell image classification are able to be used in image-based flow ...
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